def pre_process(self):
# Apply dezing to dark and flat images
inData = self.get_in_datasets()[0]
dark = inData.data.dark()
flat = inData.data.flat()
self.data_size = inData.get_shape()
pad_list = ((self.pad, self.pad), (0, 0), (0, 0))
# dezing the dark field print "*****in data shape in base filter", in_dataset[0].get_shape()
if dark.size:
(retval, self.warnflag,
self.errflag) = dezing.setup_size(dark.shape,
self.parameters['outlier_mu'],
self.pad,
mode=self.parameters['mode'])
dark = self._dezing(np.pad(dark, pad_list, mode='edge'))
inData.data.update_dark(dark[self.pad:-self.pad])
(retval, self.warnflag, self.errflag) = dezing.cleanup()
# dezing the flat field
if flat.size:
(retval, self.warnflag,
self.errflag) = dezing.setup_size(flat.shape,
self.parameters['outlier_mu'],
self.pad,
mode=self.parameters['mode'])
flat = self._dezing(np.pad(flat, pad_list, mode='edge'))
inData.data.update_flat(flat[self.pad:-self.pad])
(retval, self.warnflag, self.errflag) = dezing.cleanup()
# setup dezing for data
self._dezing_setup(self.data_size)
def pre_process(self):
# Apply dezing to dark and flat images
inData = self.get_in_datasets()[0]
dark = inData.data.dark()
flat = inData.data.flat()
pad_list = ((self.pad, self.pad), (0, 0), (0, 0))
# dezing the dark field
(retval, self.warnflag, self.errflag) = dezing.setup_size(
dark.shape, self.parameters['outlier_mu'], self.pad,
mode=self.parameters['mode'])
dark = self._dezing(np.pad(dark, pad_list, mode='edge'))
(retval, self.warnflag, self.errflag) = dezing.cleanup()
# dezing the flat field
(retval, self.warnflag, self.errflag) = dezing.setup_size(
flat.shape, self.parameters['outlier_mu'],
self.pad, mode=self.parameters['mode'])
flat = self._dezing(np.pad(flat, pad_list, mode='edge'))
(retval, self.warnflag, self.errflag) = dezing.cleanup()
# setup dezing for data
(retval, self.warnflag, self.errflag) = \
dezing.setup_size(self.data_size, self.parameters['outlier_mu'],
self.pad, mode=self.parameters['mode'])
def dezing_brickframe(pnum):
mu=1.5
instack=read_image_set(pnum)
outim=np.empty_like(instack)
tifffile.imsave("/dls/science/users/kny48981/instack.tif",instack)
dezing.setup(instack,outim,mu,2)
dezing.run(instack,outim)
dezing.cleanup(instack,outim)
tifffile.imsave("/dls/science/users/kny48981/outstack.tif",outim)
def dezing_brickframe(pnum):
mu = 1.5
instack = read_image_set(pnum)
outim = np.empty_like(instack)
tifffile.imsave("/dls/science/users/kny48981/instack.tif", instack)
dezing.setup(instack, outim, mu, 2)
dezing.run(instack, outim)
dezing.cleanup(instack, outim)
tifffile.imsave("/dls/science/users/kny48981/outstack.tif", outim)
def main():
#inim3,outim=get_image_array()
inim3,outim=construct_test_array()
tifffile.imsave("in.tif",inim3[:,:,0])
tifffile.imsave("inflop.tif",inim3[0,:,:])
print "in test_pymain: array shape is:",inim3.shape
mu=float(sys.argv[1])
npad=2
dezing.setup_size(inim3.shape,mu,npad)
dezing.run(inim3,outim)
dezing.cleanup()
tifffile.imsave("out%f.tif" % mu ,outim[10,:,:])
def main():
#inim3,outim=get_image_array()
inim3, outim = construct_test_array()
tifffile.imsave("in.tif", inim3[:, :, 0])
tifffile.imsave("inflop.tif", inim3[0, :, :])
print "in test_pymain: array shape is:", inim3.shape
mu = float(sys.argv[1])
npad = 2
dezing.setup_size(inim3.shape, mu, npad)
dezing.run(inim3, outim)
dezing.cleanup()
tifffile.imsave("out%f.tif" % mu, outim[10, :, :])
def pre_process(self):
# Apply dezing to dark and flat images (data with image key only)
inData = self.get_in_datasets()[0]
dark = inData.data.dark()
flat = inData.data.flat()
(retval, self.warnflag, self.errflag) = dezing.setup_size(
dark.shape, self.parameters['outlier_mu'], self.pad)
pad_list = ((self.pad, self.pad), (0, 0), (0, 0))
dark = self._dezing(np.pad(dark, pad_list, mode='edge'))
flat = self._dezing(np.pad(flat, pad_list, mode='edge'))
inData.meta_data.set_meta_data(
'dark', dark[self.pad:-self.pad].mean(0))
inData.meta_data.set_meta_data(
'flat', flat[self.pad:-self.pad].mean(0))
(retval, self.warnflag, self.errflag) = dezing.cleanup()
# setup dezing for data
(retval, self.warnflag, self.errflag) = \
dezing.setup_size(self.data_size, self.parameters['outlier_mu'],
self.pad)
def pre_process(self):
# Apply dezing to dark and flat images (data with image key only)
inData = self.get_in_datasets()[0]
dark = inData.data.dark()
flat = inData.data.flat()
(retval, self.warnflag,
self.errflag) = dezing.setup_size(dark.shape,
self.parameters['outlier_mu'],
self.pad)
pad_list = ((self.pad, self.pad), (0, 0), (0, 0))
dark = self._dezing(np.pad(dark, pad_list, mode='edge'))
flat = self._dezing(np.pad(flat, pad_list, mode='edge'))
inData.meta_data.set_meta_data('dark',
dark[self.pad:-self.pad].mean(0))
inData.meta_data.set_meta_data('flat',
flat[self.pad:-self.pad].mean(0))
(retval, self.warnflag, self.errflag) = dezing.cleanup()
# setup dezing for data
(retval, self.warnflag, self.errflag) = \
dezing.setup_size(self.data_size, self.parameters['outlier_mu'],
self.pad)
def post_process(self):
logging.debug("Running Dezing Cleanup")
dezing.cleanup()
logging.debug("Finished Dezing Cleanup")
def post_process(self):
(retval, self.warnflag, self.errflag) = dezing.cleanup()
def post_process(self):
(retval, self.warnflag, self.errflag) = dezing.cleanup()
def post_process(self):
logging.debug("Running Dezing Cleanup")
dezing.cleanup()
logging.debug("Finished Dezing Cleanup")
def post_process(self):
dezing.cleanup()
